Circ_0004140 promotes lung adenocarcinoma progression by upregulating NOVA2 via sponging miR-330-5p.

BACKGROUND: Circular RNAs (circRNAs) play a significant role in the tumorigenesis and progression of diverse human cancers, including lung adenocarcinoma. A previous study suggested that circ_0004140 expression was increased in lung adenocarcinoma cells. However, the molecular mechanism of circRNA circ_0004140 involved in lung adenocarcinoma is poorly defined. METHODS: Circ_0004140, microRNA-330-5p (miR-330-5p), and NOVA alternative splicing regulator 2 (NOVA2) expression were determined by real-time quantitative polymerase chain reaction (RT-qPCR). Cell proliferation, apoptosis, migration, invasion, and angiogenesis ability were assessed using 5-ethynyl-2'-deoxyuridine (EdU), flow cytometry, wound healing, transwell, and capillary-like network formation assays. Proliferating cell nuclear antigen (PCNA), cyclin D1, and NOVA2 protein levels were detected using Western blot assay. The interaction between miR-330-5p and circ_0004140 or NOVA2 was verified by dual-luciferase reporter assay. Xenograft tumor model was utilized to assess the role of circ_0004140 in tumor growth in vivo. RESULTS: Circ_0004140 was upregulated in lung adenocarcinoma tissues and cell lines. Circ_0004140 silencing suppressed cell proliferation, migration, invasion and tube formation ability, and triggered the apoptosis of lung adenocarcinoma cells. Circ_0004140 acted as a molecular sponge for miR-330-5p, and miR-330-5p silencing largely reversed circ_0004140 knockdown-induced effects in lung adenocarcinoma cells. NOVA2 was a target of miR-330-5p, and NOVA2 overexpression might largely overturn miR-330-5p overexpression-induced influences in lung adenocarcinoma cells. Circ_0004140 upregulated NOVA2 expression via sponging miR-330-5p in lung adenocarcinoma cells. Circ_0004140 silencing restrained xenograft tumor growth in vivo. CONCLUSION: Circ_0004140 knockdown might suppress the malignant biological behaviors of lung adenocarcinoma cells via miR-330-5p-dependent regulation of NOVA2.

Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data.

Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.